Aerosol elastic scatter signatures in the near- and midwave IR spectral regions
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Citation
Richardson, Jonathan M. et al. “Aerosol elastic scatter signatures
in the near- and mid-wave IR spectral regions.” Laser Radar
Technology and Applications XIV. Ed. Monte D. Turner & Gary
W. Kamerman. Orlando, FL, USA: SPIE, 2009. 73230Q-9. ©
2009 SPIE--The International Society for Optical Engineering
As Published
http://dx.doi.org/10.1117/12.819165
Publisher
The International Society for Optical Engineering
Version
Final published version
Accessed
Thu May 26 18:52:56 EDT 2016
Citable Link
http://hdl.handle.net/1721.1/52679
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Detailed Terms
Aerosol Elastic Scatter Signatures in the Near and Mid-Wave IR Spectral Regions
Jonathan M. Richardson1, John C. Aldridge, Adam B. Milstein, and Joseph J. Lacirignola
1
MIT Lincoln Laboratory, 244 Wood St., Lexington, MA 02420
[email protected]
ABSTRACT
An essential milestone in the development of lidar for biological aerosol detection is accurate characterization of agent,
simulant, and interferent scattering signatures. MIT Lincoln Laboratory has developed the Standoff Aerosol Active
Signature Testbed (SAAST) to further this task, with particular emphasis on the near- and mid-wave infrared.
Spectrally versatile and polarimetrically comprehensive, the SAAST can measure an aerosol sample’s full Mueller
Matrix across multiple elastic scattering angles for comparison to model predictions. A single tunable source covers the
1.35–5 ȝm spectral range, and waveband-specific optics and photoreceivers can generate and analyze all six classic
polarization states. The SAAST is highly automated for efficient and consistent measurements, and can accommodate a
wide angular scatter range, including oblique angles for sample characterization and very near backscatter for lidar
performance evaluation.
This paper presents design details and selected results from recent measurements.
Disclaimer: This work was sponsored by the US Army Edgewood Chemical Biological Center under Air Force
Contract FA8721-05-C-0002. Opinions, interpretations, conclusions and recommendations are those of the
authors and not necessarily endorsed by the United States Government.
1. INTRODUCTION
1.1. Background
Remote (“standoff”) detection of biological agents using light detection and ranging (lidar) offers many potential
capabilities not provided by point (localized) detection systems. Although point sensors are a more mature technology
offering higher localized sensitivity,1 standoff systems promise sensitivity over a wide area and may be simpler to
deploy where this capability is needed. A primary challenge to the development of a reliable bio-Lidar detection system,
in that the backscatter coefficient of a potentially dangerous aerosol threat could be small when compared to that of
ambient aerosols. It is thus advantageous to be able to detect a threat near its release point where the density is highest,
providing early warning to those downwind. Post attack, the standoff system provides information about what locations
have been affected, aiding in treatment and decontamination efforts. Ultimately, the best solution for protecting at-risk
locations might be an integrated system that combines high-sensitivity point sensors with a wide-area coverage standoff
detector, thus providing the best of both worlds.2
Development of standoff detections systems faces a major hurdle in the field test stage due to understandable
restrictions on the open release of bio materials. The performance of a particular system must be inferred from a
combination of laboratory measurements of threat signatures (using appropriate safety measures), field measurements
using benign agent (“simulant”) signatures, field measurements of natural and man-made backgrounds, and sensor
models. The accuracy of this approach can be verified by measuring signatures of various simulants both in the lab and
in the field.
There are various signatures that have been exploited by lidar-based standoff biodetection systems. Some existing biolidar concepts have utilized IR backscatter for ranging and sizing combined with UV fluorescence for bio
discrimination.3 The fluorescence signature has the complication that it is relatively small (when compared to
backscatter) and it lies within the solar band.4 It would thus be advantageous to develop methods that rely on infrared
signatures only. Recent work has demonstrated that polarization-sensitive infrared lidar may be useful for standoff bio
discrimination.5-8 Polarization lidar is sensitive to the size and shape of the aerosols and has been exploited for
differentiating ice crystals from water droplets in clouds.9 One feature of bio species is that their shape, at least in their
Laser Radar Technology and Applications XIV, edited by Monte D. Turner, Gary W. Kamerman,
Proc. of SPIE Vol. 7323, 73230Q · © 2009 SPIE · CCC code: 0277-786X/09/$18 · doi: 10.1117/12.819165
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natural form, is highly regular. Even in aggregate form, the underlying shape of individual species is retained. The
question is whether this signature is sufficiently selective is best determined by laboratory measurements. Once
signatures are measured in the laboratory, detector systems can be developed that exploit them to their best advantage in
the field.
Although direct measurements of particle scatter signatures are always preferable, it is very useful to develop aerosol
optical models that can predict the scatter. The utility of such models can be illustrated by considering the wide range of
conceivable transmitter wavelengths, the many threat and interferent species, the many forms they may take (e.g.,
agglomerated particles), the infinite possible aerosol mixtures, and the wide range of environmental conditions in which
they may be dispersed. Validated aerosol scattering models can help to determine which of these conditions cause
significant changes to the agent signatures. Inputs to an aerosol scattering model are the range of particulate/droplet
shapes and the real and imaginary indices of refraction. Spherical particles or droplets have optical properties that can
be calculated using the Mie method (described in numerous texts). Randomly oriented distributed-sized spheroidal
particles (somewhat akin to bacteria) can be calculated straightforwardly using the Mishchenko T-Matrix code.10
Irregular particles, such as agglomerates, can only be modeled by discrete methods such as the Discrete Dipole
Approximation (DDA),11 which involves significant computation, particularly when averaging over many possible sizes
and orientations.
MIT Lincoln Laboratory has developed the Standoff Aerosol Active Signature Testbed (SAAST) to measure the
polarization-dependent elastic scattering signatures of a variety of biological and inert aerosols over a wide range of
wavelengths.12 The SAAST is both an aerosol ellipsometer and a “lidar in a box.” The SAAST aerosol generation and
monitoring system is currently capable of producing a wide range of wet and dry simulant and background aerosols.
While the SAAST is currently approved for BL2 (non-pathogenic) biological materials only, many of the measurement
techniques would be applicable to laboratory measurements of actual threat agents (to be performed at the appropriate
institution using appropriate precautions). The SAAST is able to measure optical scattering near 180º (direct backscatter
as in a Lidar system) and also oblique angles. Although most lidar systems are only able to measure at 180º, the oblique
scattering data has proven invaluable for comparison to model predictions, as discussed above.
2. DESIGN OF THE SAAST
The design of the SAAST is shown in Figure 1. The SAAST is qualified to handle BL1/BL2 (non-pathogenic) bioaerosols and inert materials. We have used several different aerosolizer systems, both wet and dry. Not shown is the
optical receiver system, which rotates in the plane of the laser beam and can measure within 2º of direct backscatter and
within 10 º of forward at arbitrary increments in between. The optical design has been discussed more thoroughly
elsewhere.12, 13 The theory of operation is shown in Figure 2. When calibrated and when combined with a particle
counter, the SAAST determines the differential scattering cross section
the total scattering cross section
V scat ,
dV
, which can be integrated to determine
d: T
and the polarization dependence of the scatter. For lidar modeling, the only
quantities of interest are the differential backscatter cross section
Vb {
dV
, the extinction cross section V e , and
d: 180$
the depolarization ' , defined below. In the case of non-absorbing particles,
V e V scat , and the extinction can be
determined by integrating the total scatter. It is always the case that the total scattering cross section places a lower
bound on the extinction cross section, the rest being due to absorption.
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Room
Air In
SAAST
Safety
Enclosure
Aerosol
Generator
Safety
Enclosure
HEPA Input
Filter
Collison
Generator
- Column
Laser
Beam
Rotating
Brush
Generator
]
Aerosol
Plume
Aerosol Capture
Tube
MET1
Chamber
Aerosol
Monitor
Particle
Counter
HERA
Figure 1: The SAAST BL2 bio
and inert aerosol generation and
conditioning system. Aerosol
samples are generated in a liquid
carrier. They then travel through
a column and are delivered to an
interaction region via a 1cm tube
where they pass through the laser
beam. The sample is then
captured in a HEPA filtration
cartridge for disposal.
TS1302A
Cartridge I
100:1 Diluter
HERA
Cartridge 2
TS13321
Particle Sizer/
Counter
Laser
Pol Gen
Figure 2: Theory of Operation:
The SAAST transmits laser light
with a known polarization state
into the sample and resolves the
polarization state of the scattered
light, allowing for a
determination of the Mueller
Scattering Matrix and, when
calibrated, scattering cross
sections. The scattering angle can
be varied from 10º to 178º
4 Scattering Angle
Sample
Pola
rime
ter
~
S SCA 4, O [ N CScat F 4, O ˜ S INC O Scattered
Detector
Stokes
Efficiency
Vector
Mueller
Cross
Num
Scattering
Particles Section
Matrix
Incident
Stokes
Vector
For aerosols of axial symmetric randomly oriented particles, the Mueller matrix takes on a simplified form as shown
below.
M (4, O )
ª M 11
«M
« 12
« 0
«
¬ 0
M 12
M 22
0
0
0
0
M 33
M 34
0 º
0 »
»
M 34 »
»
M 44 ¼
M11 is known as the scatter function since it describes the relative fraction scattered at each angle.
At back angles, the form is even simpler14
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(1)
M (180$ , O )
0
0
0 º
ª1
«0 1 '
0
0 »
»
M 11 «
0
' 1
0 »
«0
«
»
0
0
2 ' 1¼
¬0
(2)
Where ' 1 M 22 M 11 is called the depolarization factor15.
For collections of spheres (e.g., droplets), the Mueller scattering matrix is simplified again
M sph ( 4, O )
ª M 11
«M
« 12
« 0
«
¬ 0
M 12
M 11
0
0
0
M 33
0
M 34
0 º
0 »
»
M 34 »
»
M 33 ¼
(3)
Perfect spheres, unlike all non-spherical particles with sizes comparable or larger than the wavelength, cause no
depolarization at back angles15 (e.g., ' 0 ), so M sph (180$ , O ) M 11 I 4u4 , where I is the identity matrix. This has led to
the use of ' as a bio signature, since bacteria and spores are inherently non-spherical. 7
3. RESULTS
The SAAST is currently involved in an extensive campaign to measure simulant and interferent scattering signatures at
1.55 and 3.4 microns. These signatures are expressed as angle-dependent Mueller scattering matrices, which can be
represented as a collection of 6 unique plots, each showing an element of the Mueller matrix vs. scattering angle.13 It is
common to normalize the unique elements by the scatter function (M11), except for M11 itself. There are this six
resulting plots that give all the necessary scattering information as shown below.
Our first example is SAAST scattering data taken with an aerosolized collection of 3 micron polystyrene spheres (Duke
Scientific) as shown in Figure 3. The data is shown with a comparison to a Mie theory calculation performed for a
Gaussian distribution with mean diameter 2.97 microns, standard deviation of .7%, and refractive index of 1.57. The
sample is specified to have a mean diameter of 3 microns with <5% standard deviation. Particulars of the fitting
procedure are shown in Figure 4. All the elements shown are included in the Chi squared calculation except M11. M11
(the scatter function) is not included because this element is highly sensitive to systematic effects (such as aerosol
density shifts) during the measurement. The fit to theory is highly sensitive to the mean diameter and real index,
providing a viable method for determining both with high accuracy. We also find that the width in the size distribution
is much less than the 5% outside tolerance specified.
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M22/M11 Caic (Blue) and Measured (Red)
Mu Caic (Blue) and Measured (Red)
M33/M11 Caic (Blue) and Measured (Red)
2.0
1.0
.5
1.5
0.5
4I {I
.1
.05
10
I
0.0
.01
0.5
SE)
2E-3
60
30
90
150
120
0.0
180
50
Scattering Angle
100
M44/M11 Caic (Blue) and Measured (Red)
100
50
150
150
Scattering Angle
Scattering Angle
M12/M11 Caic (Blue) and Measured (Red
M34/M11 Caic (Blue) and Measured (Red
0
1.0
1.0
0.5
0.5
0.5
0.0
0.0
50
100
0.0
50
150
Scattering Angle
100
150
Scattering Angle
50
100
150
Scattering Angle
Figure 3: Measured Mueller matrix elements of 3 micron diameter polystyrene spheres at a laser wavelength of
1.55 microns shown with a fit to a Mie-theory calculation as described in the text. M11 is also know as the scatter
function and has been used to normalize the 5 other elements.
Results of Fit to Mie Theory
•
Elements included: M12, M21, M34, M43, M33, M44
&2 Contours:
M12
M21
•
M33
M34
M43
M44
Figure 4: Particulars about the
fit of the data above. The fit is at
a local minimum of the & 2 for
both the diameter and real index
of refraction. The fit of the data
to theory provides a highly
sensitive measurement of the
mean size, the variation in size,
and the real index of refraction.
Scatter Function (M11) is susceptible to systematic
errors and was thus omitted from fit
–
Comparison to M11 using best fit parameters is good
Best Fit Data
Measured
Expected
Mean Radius
1.485 Pm
1.5 Pm
Radial Spread
<.7%
<5%
Refractive Index
1.57
~1.6
~1.57[1]
Systems-9
DPG 10/17/2008
MIT Lincoln Laboratory
[1] X. Ma, et al., Physics in Medicine and Biology, vol. 48, pp. 4165-4172, 2003
We have previously presented SAAST measurements of bacterial spores13. These measurements were uncalibrated and
allowed only for the determination of the Mueller scattering matrix. The data shown in Figure 5 was recently acquired
using the SAAST at a wavelength of 1.55 μm using liquid-grown Bacillus atrophaeus (Bg). We have recently
completed a calibration of the SAAST instrument using a calibrated backscatter target from Sphereoptics, Inc. It is then
possible to get the single-particle differential cross section using the particle count provided by the commercial TSI
aerodynamic particle counter depicted in Figure 1. The differential cross section can then be integrated to find the total
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scattering cross section as shown in Figure 6. This is a polar integral, meaning that it will be weighted by Sin(T), which
goes to zero at 0º and 180º. The procedure we have used is to fit a smooth function to the Sin(T)-weighted data,
including the added zero values, allowing for a straightforward integration of the result to give the total scattering cross
section. There are several sources of systematic error in these calculations which are still under study at this time, one of
which is the overlap between the laser beam and aerosol plume. We also should have acquired additional data points at
the peak of the Sin(T)-weighted curve. These uncertainties could lead to a systematic error of at least ±50% on the cross
sections, which will be studied in future work.
As in previous work, this data has been compared to an ellipsoidal model, which has been calculated using the
Mishchenko T-matrix code, showing that the scatter depends sensitively on the size and shape of the aerosol particles.
(Please pardon the use of F rather than M for the elements of the Mueller scattering matrix.) Once again, we find the
model parameters that minimize the Chi-squared fitting criterion using all data shown except that of F11, since this
element is thought to be more susceptible to systematic error. As shown in the figure, F11 (the scatter function) has been
normalized to give the differential cross section. At 180º, this is the backscatter cross section in units of Pm2 per
steradian. The other elements have all been normalized by F11 and are thus unitless. The fitting process is limited by the
capability of the Mishchenko code to converge for particles with aspect ratios above ~2.5:1. We are currently
addressing this deficiency using the discrete modeling method outlined below. The model also predicts the cross
sections, which can be compared with the measurements. The comparison of the backscatter cross section is very good,
whereas the model prediction of the total scatter cross section is approximately twice that measured.
F11 vs Angle
F22/F11 vs Angle
F33/F11 vs Angle
1.1
Depol
1
0.8
1
0.6
0.4
F22/F11
F11
0.9
F33/F11
-1
10
0.8
0.2
0
-0.2
-0.4
0.7
-0.6
-2
-0.8
10
0
45
90
135
Angle (degrees)
180
0.6
0
Vb
45
F12/F11 vs Angle
90
135
Angle (degrees)
180
0
45
F34/F11 vs Angle
180
F44/F11 vs Angle
0.1
0
90
135
Angle (degrees)
1
-0.1
0
0.5
-0.3
-0.4
F44/F11
F34/F11
F12/F11
-0.2
-0.1
0
-0.2
-0.5
-0.5
-0.6
-0.3
-0.7
0
45
90
135
Angle (degrees)
180
0
45
90
135
Angle (degrees)
180
0
45
90
135
Angle (degrees)
180
Figure 5: Scatter data from Bacillus atrophaeus (Bg) with fit to a T-matrix ellipsoidal model calculated using the
Mishchenko code. Model parameters are: Geometric mean diameter: 0.91 um, Geometric Stdev: 1.1 (unitless), and
Aspect ratio: 0.42. (Please pardon the use of F rather than M for the elements of the Mueller scattering matrix.)
Proc. of SPIE Vol. 7323 73230Q-6
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Bg Spore Cross Sections
Oflr.p*S Ct.tt Snhlon of SO ,MØRN,
• Backscatter cross section
Vb = 1.0 u 10-10 cm 2/str
• Depolarization: 14 ± 7%
• Weight scatter function data by
Sin[th] and integrate to obtain
the total scattering cross
section
V scat = 7.5 u
10-9
II.
3D
It44WIgMd S(MI.. FWKSn f tg ,NgF.M4.
cm2
• Model prediction
Figure 6: The SAAST can be
calibrated such that it is possible
to determine the backscatter and
total cross sections. The cross
sections
predicted by the
ellipsoidal model compare well
with the measured values.
Vb = 1.1 u 10-10 cm2/str
Vscat = 11 u 10-9 cm2
–
–
–
Depol: 14.7%
MIT Lincoln Laboratory
BC-16
AM 2/25/2009
The single spore data shown above is a very special case for a bio aerosol, which can easily agglomerate into particles
with several spores. Many such samples have been investigated as part of our measurements program and one example
is shown in Figure 8. We are currently developing a system for modeling the scatter from multi-spore clusters as
illustrated in Figure 7. Due to the substantial amount of computation that is required, a key enabling technology of this
method is LLGRID16, the high performance, on-demand parallel computing system developed recently at MIT Lincoln
Laboratory. We start by obtaining scanning electron microscope (SEM) images of the particulates, which can be used to
infer certain spore geometric properties (for example, the aspect ratio). We then model an ensemble of spore clusters by
generating numerous instances of discretized volumes consisting of joined spheroid shapes, with the spheroids oriented
randomly relative to each other and drawn from a range of sizes. The entire ensemble of modeled spore clusters is then
divided among several parallel computing nodes. The Mueller scattering matrix from each individual spore cluster is
computed using the Amsterdam DDA code17. The results from all spore clusters are then aggregated together and
averaged using a size distribution which can be adjusted after the fact for optimizing the fit to measured data.
Dry Bg spores clusters
Results
10
2
10
1
10
0
Computational Models
LLGRID
Figure 7: Illustration of the process used to
develop a model for multi-spore clusters.
F22/F11 vs Scattering Angle
F11 vs Scattering Angle
1.4
F11
F22/F11
1.2
1
0.8
0.6
10
-1
0
50
100
150
Scattering Angle (degrees)
0.4
0
50
100
150
Scattering Angle (degrees)
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Figure 8 shows some initial example results, including the measured SAAST Mueller scattering matrix data for a dry Bg
spore sample, and the best fits over size distribution to three different scattering models. The models include Mie theory
(i.e., spheres), T-matrix theory (i.e., singleton spheroids) and the DDA (in this case, 3-spheroid clusters). For the Tmatrix and the DDA, an aspect ratio of 2.17 to 1 was assumed for consistency with the SEM imagery. To obtain the best
fit, a lognormal distribution for each spore’s shape was assumed, and the mean and standard deviation which provided
the best Chi-squared fit to the measured data were computed. The use of the DDA 3-spheroid model resulted in
significant improvements in fitting to the measured data over the other two methods. In particular, note the improved fit
to M22/M11 (representing shape- and size-dependent depolarization) and to M11 (representing the size-dependent scatter
function). We are currently refining our microscopy techniques to obtain statistically representative images of the
particle ensemble after the sample has been measured in the SAAST. This will provide for detailed comparisons
between the estimated particle geometries and the geometries observed in the SEM images.
F22/F11 vs Scattering Angle
F11 vs Scattering Angle
2
10
F33/F11 vs Scattering Angle
1.4
1.5
1.2
1
1
0.5
F22/F11
F11
0
F33/F11
1
10
0.8
10
0.6
-1
10
0
0.4
0
50
100
150
Scattering Angle (degrees)
-0.5
-1
0
50
100
150
Scattering Angle (degrees)
0.4
0.2
1
0.2
0.1
0
-0.5
-1
0
0
F34/F11
0.5
-0.2
-0.6
0
0
-0.1
-0.2
-0.4
50
100
150
Scattering Angle (degrees)
50
100
150
Scattering Angle (degrees)
F34/F11 vs Scattering Angle
F12/F11 vs Scattering Angle
1.5
F12/F11
F44/F11
F44/F11 vs Scattering Angle
0
50
100
150
Scattering Angle (degrees)
-0.3
0
50
100
150
Scattering Angle (degrees)
SAAST data
Mie (1 sphere)
T-matrix (1 spheroid)
DDA (3 spheroids)
Figure 8: Scattering from a multi-spore Bg sample with comparison to Mie (spherical), T-Matrix (spheroidal) and multispore models. Note improvement in the element M22 fit due to the DDA model. Also, note that the measured element
F11 has been renormalized such that the curves artificially overlie.
4. CONCLUSIONS
The SAAST has been designed and constructed as a versatile optical signature testbed for aerosol characterization,
demonstrating both aerosol ellipsometry and “lidar-in-a-box” capability. Only two example studies are presented in this
report, whereas many studies have been performed on various samples at both NIR and MWIR wavelengths. Easily
adaptable for other missions, the SAAST is currently tasked with the measurement of infrared polarization
phenomenology for biological agent detection. The polarization signature offers the possibility of discriminating certain
bio-threats from backgrounds using IR radiation, avoiding most of the background radiation present in sunlight. The
testbed is specially designed to measure both back- and oblique- angle polarization signatures, which are respectively
applicable to Lidar signatures and useful for validating model predictions. Measurements from the SAAST have
compared well with model predictions. The SAAST is continuously undergoing improvements, upgrades, and
extensions and is currently being used for an extensive data collection campaign using a wide variety of biological
simulants and interferents.
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Aerosol elastic scatter signatures in the near- and mid- Please share